scholarly journals Mobile health technology: a novel tool in chronic disease management

2021 ◽  
Author(s):  
Kaman Fan ◽  
Yi Zhao
Keyword(s):  
Chronic Disease ◽  
Disease Management ◽  
Mobile Health ◽  
Chronic Disease Management ◽  
Health Technology ◽  
Mobile Health Technology

scholarly journals Incorporating Behavioral Trigger Messages Into a Mobile Health App for Chronic Disease Management: Randomized Clinical Feasibility Trial in Diabetes

10.2196/15927 ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. e15927
Author(s):  
Scott Sittig ◽  
Jing Wang ◽  
Sriram Iyengar ◽  
Sahiti Myneni ◽  
Amy Franklin
Keyword(s):  
Chronic Disease ◽  
Disease Management ◽  
Mobile Health ◽  
Self Efficacy ◽  
Chronic Disease Management ◽  
Text Messages ◽  
Feasibility Trial ◽  
Significant Difference ◽  
Within Subjects ◽  
The Impact

Background Although there is a rise in the use of mobile health (mHealth) tools to support chronic disease management, evidence derived from theory-driven design is lacking. Objective The objective of this study was to determine the impact of an mHealth app that incorporated theory-driven trigger messages. These messages took different forms following the Fogg behavior model (FBM) and targeted self-efficacy, knowledge, and self-care. We assess the feasibility of our app in modifying these behaviors in a pilot study involving individuals with diabetes. Methods The pilot randomized unblinded study comprised two cohorts recruited as employees from within a health care system. In total, 20 patients with type 2 diabetes were recruited for the study and a within-subjects design was utilized. Each participant interacted with an app called capABILITY. capABILITY and its affiliated trigger (text) messages integrate components from social cognitive theory (SCT), FBM, and persuasive technology into the interactive health communications framework. In this within-subjects design, participants interacted with the capABILITY app and received (or did not receive) text messages in alternative blocks. The capABILITY app alone was the control condition along with trigger messages including spark and facilitator messages. A repeated-measures analysis of variance (ANOVA) was used to compare adherence with behavioral measures and engagement with the mobile app across conditions. A paired sample t test was utilized on each health outcome to determine changes related to capABILITY intervention, as well as participants’ classified usage of capABILITY. Results Pre- and postintervention results indicated statistical significance on 3 of the 7 health survey measures (general diet: P=.03; exercise: P=.005; and blood glucose: P=.02). When only analyzing the high and midusers (n=14) of capABILITY, we found a statistically significant difference in both self-efficacy (P=.008) and exercise (P=.01). Although the ANOVA did not reveal any statistically significant differences across groups, there is a trend among spark conditions to respond more quickly (ie, shorter log-in lag) following the receipt of the message. Conclusions Our theory-driven mHealth app appears to be a feasible means of improving self-efficacy and health-related behaviors. Although our sample size is too small to draw conclusions about the differential impact of specific forms of trigger messages, our findings suggest that spark triggers may have the ability to cue engagement in mobile tools. This was demonstrated with the increased use of capABILITY at the beginning and conclusion of the study depending on spark timing. Our results suggest that theory-driven personalization of mobile tools is a viable form of intervention. Trial Registration ClinicalTrials.gov NCT04132089; http://clinicaltrials.gov/ct2/show/NCT004122089

scholarly journals SAT0652-HPR CHRONIC DISEASE MANAGEMENT AND HEALTH TECHNOLOGY READINESS OF PATIENTS WITH SYSTEMIC SCLEROSIS IN SWITZERLAND – A CROSS-SECTIONAL STUDY

2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 1285.1-1285
Author(s):  
A. Kocher ◽  
M. Simon ◽  
C. Chizzolini ◽  
O. Distler ◽  
A. A. Dwyer ◽  
...  
Keyword(s):  
Chronic Disease ◽  
Systemic Sclerosis ◽  
Disease Management ◽  
Chronic Disease Management ◽  
Health Technology ◽  
Self Management ◽  
Technology Readiness ◽  
Management Support ◽  
Research Support ◽  
Cross Sectional

Background:People living with systemic sclerosis (SSc) often lack access to coordinated, specialized care and self-management support from qualified healthcare professionals. Such gaps lead to significant unmet health needs and inability to get preventive services. The Chronic Care Model (CCM) has been used to guide disease management across a wide range of chronic conditions. The CCM often uses e-health technologies to address self-management problems, connect patients with clinicians and reduce patient travel requirements.Objectives:To evaluate current SSc care practice patterns and elicit patient health technology readiness to define relevant aspects and resources needed to improve SSc chronic disease management.Methods:We employed a cross-sectional survey using the 20-item Patient Assessment of Chronic Illness Care (PACIC) instrument to assess how aspects of SSc care align with key components of the CCM.1Six items drawn from the ‘5A’ (ask, advise, agree, assist, and arrange) model of behavioural counselling were included (all 26 items scored on 5-point scale, 1=never to 5=always). Acceptance of health technology was evaluated by adapting and combining questionnaires from Vanhoof2and Halwas3. German and French speaking SSc patients (>18 years) were recruited from university/cantonal hospitals and the Swiss scleroderma patients’ association. Participants completed anonymous paper/online questionnaires. Data were analysed descriptively.Results:Of 101 SSc patients, most were female (76%), spoke German (78%) and had a median age of 60 years (IQR: 50-68). Median disease duration was 8 years (IQR: 5-15), spanning a range of severity (31% limited SSc, 36% diffuse SSc, 3% overlap syndrome). One-quarter (25%) did not know their disease subset.The mean overall PACIC score was relatively low (2.91±0.95) indicating that care was ‘never’ to ‘generally not’ aligned with the CCM. Lowest mean subscale scores related to Follow-up/ Coordination (2.64±1.02), Goal setting (2.68±1.07) and Problem-solving/Contextual Counselling (2.94±1.22). The single items ‘Given a copy of my treatment plan’ (1.99±1.38) and ‘Encouraged to attend programs in the community’ (1.89±1.16) were given the lowest ratings. The ‘5A’ summary score was 2.84±0.97.In terms of technology readiness, 43% completed the survey online. Most participants owned a smartphone (81%), laptop (63%) and/or desktop computer (46%). The overwhelming majority of patients (91%) reported using the Internet in the last year – primarily for communication (e.g. emails, text messages). Participants indicated relatively little experience with e-health applications and participating in SSc online forums or self-help groups.Conclusion:To improve chronic disease management of SSc patients in Switzerland, current care practices warrant reengineering taking CCM components into account. Specific unmet needs relate to self-management support, help patients set individualized goals, and coordinate continuous care. Web-based technologies incorporating user-centred design principles may be a reasonable option for improving care.References:[1]Glasgow, RE, et al. Development and validation of the Patient Assessment of Chronic Illness Care (PACIC).Med Care2005; 43(5): 436-44[2]Vanhoof, JM, et al. Technology Experience of Solid Organ Transplant Patients and Their Overall Willingness to Use Interactive Health Technology. J Nurs Scholarsh2018; 50(2): 151-62[3]Halwas, N, et al. eHealth literacy, Internet and eHealth service usage: a survey among cancer patients and their relatives. J Cancer Res Clin Oncol2017; 143(11): 2291-99Disclosure of Interests:Agnes Kocher Grant/research support from: Sandoz to support the development of an eLearning module for patients with rheumatic diseases., Michael Simon: None declared, Carlo Chizzolini Consultant of: Boehringer Ingelheim, Roche, Oliver Distler Grant/research support from: Grants/Research support from Actelion, Bayer, Boehringer Ingelheim, Competitive Drug Development International Ltd. and Mitsubishi Tanabe; he also holds the issued Patent on mir-29 for the treatment of systemic sclerosis (US8247389, EP2331143)., Consultant of: Consultancy fees from Actelion, Acceleron Pharma, AnaMar, Bayer, Baecon Discovery, Blade Therapeutics, Boehringer, CSL Behring, Catenion, ChemomAb, Curzion Pharmaceuticals, Ergonex, Galapagos NV, GSK, Glenmark Pharmaceuticals, Inventiva, Italfarmaco, iQvia, medac, Medscape, Mitsubishi Tanabe Pharma, MSD, Roche, Sanofi and UCB, Speakers bureau: Speaker fees from Actelion, Bayer, Boehringer Ingelheim, Medscape, Pfizer and Roche, Andrew A. Dwyer: None declared, Peter Villiger Consultant of: MSD, Abbvie, Roche, Pfizer, Sanofi, Speakers bureau: Roche, MSD, Pfizer, Ulrich Walker Grant/research support from: Ulrich Walker has received an unrestricted research grant from Abbvie, Consultant of: Ulrich Walker has act as a consultant for Abbvie, Actelion, Boehringer Ingelheim, Bristol-Myers Squibb, Celgene, MSD, Novartis, Pfizer, Phadia, Roche, Sandoz, Sanofi, and ThermoFisher, Paid instructor for: Abbvie, Novartis, and Roche, Speakers bureau: Abbvie, Actelion, Bristol-Myers Squibb, Celgene, MSD, Novartis, Pfizer, Phadia, Roche, Sandoz, and ThermoFisher, Dunja Nicca: None declared

scholarly journals Incorporating Behavioral Trigger Messages Into a Mobile Health App for Chronic Disease Management: Randomized Clinical Feasibility Trial in Diabetes (Preprint)

2019 ◽  
Author(s):  
Scott Sittig ◽  
Jing Wang ◽  
Sriram Iyengar ◽  
Sahiti Myneni ◽  
Amy Franklin
Keyword(s):  
Chronic Disease ◽  
Disease Management ◽  
Mobile Health ◽  
Self Efficacy ◽  
Chronic Disease Management ◽  
Text Messages ◽  
Feasibility Trial ◽  
Significant Difference ◽  
Within Subjects ◽  
The Impact

BACKGROUND Although there is a rise in the use of mobile health (mHealth) tools to support chronic disease management, evidence derived from theory-driven design is lacking. OBJECTIVE The objective of this study was to determine the impact of an mHealth app that incorporated theory-driven trigger messages. These messages took different forms following the Fogg behavior model (FBM) and targeted self-efficacy, knowledge, and self-care. We assess the feasibility of our app in modifying these behaviors in a pilot study involving individuals with diabetes. METHODS The pilot randomized unblinded study comprised two cohorts recruited as employees from within a health care system. In total, 20 patients with type 2 diabetes were recruited for the study and a within-subjects design was utilized. Each participant interacted with an app called capABILITY. capABILITY and its affiliated trigger (text) messages integrate components from social cognitive theory (SCT), FBM, and persuasive technology into the interactive health communications framework. In this within-subjects design, participants interacted with the capABILITY app and received (or did not receive) text messages in alternative blocks. The capABILITY app alone was the control condition along with trigger messages including spark and facilitator messages. A repeated-measures analysis of variance (ANOVA) was used to compare adherence with behavioral measures and engagement with the mobile app across conditions. A paired sample <i>t</i> test was utilized on each health outcome to determine changes related to capABILITY intervention, as well as participants’ classified usage of capABILITY. RESULTS Pre- and postintervention results indicated statistical significance on 3 of the 7 health survey measures (general diet: <i>P</i>=.03; exercise: <i>P</i>=.005; and blood glucose: <i>P</i>=.02). When only analyzing the high and midusers (n=14) of capABILITY, we found a statistically significant difference in both self-efficacy (<i>P</i>=.008) and exercise (<i>P</i>=.01). Although the ANOVA did not reveal any statistically significant differences across groups, there is a trend among spark conditions to respond more quickly (ie, shorter log-in lag) following the receipt of the message. CONCLUSIONS Our theory-driven mHealth app appears to be a feasible means of improving self-efficacy and health-related behaviors. Although our sample size is too small to draw conclusions about the differential impact of specific forms of trigger messages, our findings suggest that spark triggers may have the ability to cue engagement in mobile tools. This was demonstrated with the increased use of capABILITY at the beginning and conclusion of the study depending on spark timing. Our results suggest that theory-driven personalization of mobile tools is a viable form of intervention. CLINICALTRIAL ClinicalTrials.gov NCT04132089; http://clinicaltrials.gov/ct2/show/NCT004122089

scholarly journals The Use of Health IT for Chronic Disease Management: A comprehensive review of diabetes applications used for self-management of the disease (Preprint)

2019 ◽  
Author(s):  
Lorrin Robinson ◽  
Jamesa Hogges ◽  
Ingrid Brown ◽  
Kennedy Craig ◽  
Akasha Lawrence ◽  
...  
Keyword(s):  
Chronic Disease ◽  
Disease Management ◽  
Mobile Health ◽  
Chronic Disease Management ◽  
The Self ◽  
Self Management ◽  
Consumer Reviews ◽  
Health Apps ◽  
Apple Ios ◽  
Mobile Health Apps

BACKGROUND Mobile health (mHealth) smartphone applications (apps) have shown promise in the self-management of chronic disease. Management of key disease variances can be performed through these applications to increase patient engagement in disease self-management. In today’s oversaturated health app market, what selection criteria do consumers employ to choose mobile health apps for disease self-management? App quality is critical in monitoring disease controls but is often linked to consumer popularity rather clinical recommendations of effectiveness in disease management. This paper provides a comprehensive review of features found in mobile health apps frequently used in the self-management of diabetes. OBJECTIVE The objective of this study was to review features of frequently used and high consumer-rated mobile health apps used in the self-management of diabetes within the Apple iOS store. These applications were cross-referenced against high consumer-rated health apps found in other online diabetes sources. This study aimed to highlight key features of consumer-favored mobile health apps used in the self-management of diabetes. METHODS A primary Apple iOS store search was conducted using the term “diabetes apps” on an Apple iPad. The top five most frequently used mobile health apps were identified and rated by the number of consumer reviews, application ratings, and the presence of key diabetes management features: dietary blood glucose, A1C, insulin, physical activity and prescription medication. A subsequent Google search was conducted using the search term “best Apple diabetes apps”. The top three search results – Healthline, Everyday Health, and Diabetes Apps, American Diabetes Association – were explored. The top five frequently used apps among those sources were examined against the same Apple iOS criteria. RESULTS Twelve mobile health apps were reviewed in total due to repetition in popularity across the four evaluated sources. Only one health app – Glucose Buddy Diabetes Tracker – appeared most frequently used within the Apple iOS store and across the other three sources. The OneTouch Reveal app ranked first on the list in the iOS store with 39,000 consumer reviews and a rating of 4.7 out of 5.0 stars while only appearing once among the other sources. Blood glucose tracking was evident across all apps, but other disease management features varied in type with at least three of the five key features being present across the 12 reviewed apps. Subscription costs and integration needs were present which could play a major role in consumer app selection. While mobile app preference was assessed and defined by the number of consumer reviews and star ratings, there were no scientific standards used in the selection and ranking of the health apps within this study. CONCLUSIONS Mobile health applications (apps) have shown promise in chronic disease management, but a surge in development of these non-regulated health solutions points to a need for standards in quality. A governing body of health information technology, clinical, policymaking, and other industry stakeholders, including patients, could be beneficial in defining health application standards for effective chronic disease management. Variabilities in features, cost, and other management inconsistencies could be diminished by regulatory uniformity and increase both patient engagement activities and disease outcomes.

scholarly journals A Personalized Mobile Health Program for Type 2 Diabetes During the COVID-19 Pandemic: Single-Group Pre–Post Study (Preprint)

2020 ◽  
Author(s):  
Ian Yi Han Ang ◽  
Kyle Xin Quan Tan ◽  
Clive Tan ◽  
Chiew Hoon Tan ◽  
James Wei Ming Kwek ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Chronic Disease ◽  
Disease Management ◽  
Mobile Health ◽  
Intervention Program ◽  
Chronic Disease Management ◽  
Diabetes Management ◽  
Mobile App ◽  
Diabetes Status

BACKGROUND With increasing type 2 diabetes prevalence, there is a need for effective programs that support diabetes management and improve type 2 diabetes outcomes. Mobile health (mHealth) interventions have shown promising results. With advances in wearable sensors and improved integration, mHealth programs could become more accessible and personalized. OBJECTIVE The study aimed to evaluate the feasibility, acceptability, and effectiveness of a personalized mHealth-anchored intervention program in improving glycemic control and enhancing care experience in diabetes management. The program was coincidentally implemented during the national-level lockdown for COVID-19 in Singapore, allowing for a timely study of the use of mHealth for chronic disease management. METHODS Patients with type 2 diabetes or prediabetes were enrolled from the Singapore Armed Forces and offered a 3-month intervention program in addition to the usual care they received. The program was standardized to include (1) in-person initial consultation with a clinical dietitian; (2) in-person review with a diabetes specialist doctor; (3) 1 continuous glucose monitoring device; (4) access to the mobile app for dietary intake and physical activity tracking, and communication via messaging with the dietitian and doctor; and (5) context-sensitive digital health coaching over the mobile app. Medical support was rendered to the patients on an as-needed basis when they required advice on adjustment of medications. Measurements of weight, height, and glycated hemoglobin A<sub>1c</sub> (HbA<sub>1c</sub>) were conducted at 2 in-person visits at the start and end of the program. At the end of the program, patients were asked to complete a short acceptability feedback survey to understand the motivation for joining the program, their satisfaction, and suggestions for improvement. RESULTS Over a 4-week recruitment period, 130 individuals were screened, the enrollment target of 30 patients was met, and 21 patients completed the program and were included in the final analyses; 9 patients were lost to follow-up (full data were not available for the final analyses). There were no differences in the baseline characteristics between patients who were included and excluded from the final analyses (age category: <i>P</i>=.23; gender: <i>P</i>=.21; ethnicity: <i>P</i>&gt;.99; diabetes status category: <i>P</i>=.52, medication adjustment category: <i>P</i>=.65; HbA<sub>1c</sub> category: <i>P</i>=.69; BMI: <i>P</i>&gt;.99). The 21 patients who completed the study rated a mean of 9.0 out of 10 on the Likert scale for both satisfaction questions. For the Yes-No question on benefit of the program, all of the patients selected “Yes.” Mean HbA<sub>1c</sub> decreased from 7.6% to 7.0% (<i>P</i>=.004). There were no severe hypoglycemia events (glucose level &lt;3.0 mmol/L) reported. Mean weight decreased from 76.8 kg to 73.9 kg (<i>P</i>&lt;.001), a mean decrease of 3.5% from baseline weight. Mean BMI decreased from 27.8 kg/m<sup>2</sup> to 26.7 kg/m<sup>2</sup> (<i>P</i>&lt;.001). CONCLUSIONS The personalized mHealth program was feasible, acceptable, and produced significant reductions in HbA<sub>1c</sub> (<i>P</i>=.004) and body weight (<i>P</i>&lt;.001) in individuals with type 2 diabetes. Such mHealth programs could overcome challenges posed to chronic disease management by COVID-19, including disruptions to in-person health care access. CLINICALTRIAL

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